5X

We're unapologetically remote!

Join our webinar on 29th August at 9:30 AM PST to learn how data teams can reduce platform expenses without sacrificing performance. Tarush Aggarwal will share real-world examples and insights on optimizing data pipelines, leveraging Apache Iceberg, and more. Registration link in the comments section 👇

After leaving WeWork, I was burnt out and looking to take a break from the industry. Despite my hesitation I was constantly bombarded with meeting requests from CEOs, startup founders, and banking execs, marketers. They were all wrestling with the same  challenge: “We're drowning in data—but how do we use it?” My pitch to all of them was the same "Focus on building a data platform, here are a list of vendors you can work with. Next integrate all of your source systems. List down 10 most important business goals and work backwards to build the data models and dashboards to support them. Only when this is stable for 6 months can you think about AI/ML" Basically, here are some car parts, go and assemble a car from scratch. That’s when it clicked. Why not build a complete platform that works right away, like a car that’s ready to drive off the lot? For 95% of companies building a platform from scratch makes absolutely no sense. Fast forward, 3 years and here we are at 5X, turning that vision into reality. Incredibly excited to announce our next gen platform that is completely self-service. No assembly required! Try building your first use case on 5X. You might be surprised to find that tasks that would take months can now be built in under 30 minutes Self service link on our website or in the comments. Take it for a spin and dm or email me at [email protected] with your thoughts.

Join our webinar to learn strategies for creating effective data teams that drive success. Gain insights from Pamela Zirpoli, Director of Data Quality & Integrity at Warner Bros. Discovery and Tarush Aggarwal, CEO at 5X! Register now: https://lnkd.in/ekktV6QG #DataCamp #DataTeams #DataScience #Webinar #Leadership

"We already have a data team; why do we need help with data?" – a question I always hear on my prospect calls.  Think about it: How many times have you rushed to deliver analysis without a solid and comprehensive data foundation? Would you build a skyscraper on shaky ground? Of course not! But that's what happens when companies neglect their core data foundation. You might see some initial results, but soon enough, progress slows to a crawl, and the whole thing risks collapse. That's where 5X comes in! We have encountered many cases of helping clients revamp the data warehouse (structure, naming conventions, modeling approach, etc.), which is a mess from all those ad-hoc requests or legacy tech debts.  We specialize in building the foundational data infrastructure you need to not only execute your current projects but also build scalable data architectures that endure.  We are here to shift the narrative from reactive data usage to proactive data strategy. Whether you're starting from scratch or enhancing existing capabilities, investing in a solid data infrastructure ensures sustainable growth. So, if you're ready to take your data game to the next level, let's chat! We'll show you how to create a data foundation that's as reliable as it is powerful. #data #datainfrastructure #datastrategy #5X

Remember when spreadsheets were enough for your startup's data needs? Yeah, me neither. 😛   Today's startups are juggling more than a dozen different data sources, making it harder than ever to get a clear picture of what's happening in your business. Enter the #moderndatastack, and here’s why it’s crucial: 1️⃣ Unified Data Warehouse: Store data from all sources into a central data warehouse to have single source truth. This is the foundation for basic reporting and advanced data products like ML models and recommendations. 2️⃣ Nimble Infrastructure: The backbone of a data-driven company is its infrastructure. A modern data stack is modular and flexible, allowing you to swap components as needed while ensuring each tool excels at its specific function. This flexibility is key to adapting to evolving needs without breaking the bank. 3️⃣ Efficiency and Scalability: Modern data stacks turbocharge your data team's workflow, making it faster and more scalable. Early-stage startups quickly outgrow spreadsheets, and a modern stack helps maintain efficiency and a single source of truth, reducing engineering bottlenecks. 4️⃣ Future-Proofing: Choose the best tools for the job, avoid vendor lock-in, and scale your data stack as your company grows. This ensures consistent access to clean, reliable data, even as your needs evolve. If building a modern data stack feels overwhelming, don't worry – we've got your back. At 5X, we have developed solutions that will help you with everything you need to turn data into dollars, from infrastructure setup to ongoing support. Ready to transform your data into a growth engine? Let's chat about how 5X can help you take your startup to the next level. 🚀

Your data platform is sabotaging your AI dreams. Traditional data readiness was just about cleaning up your data for basic analytics and dashboards. However today data readiness has one other stakeholder: AI And we've all read that you can't have an AI strategy without a data strategy and if you dont understand your data neither will AI... So, how do you get your data AI-ready? Here are the 6 steps involved. 1. Automated Ingestion: 2. Leverage Engagement Data 3. Model Your Data 4. Metrics & Iterations 5. Semantic Layer 6. Business Process Want to learn more about how to get your data AI-ready? In my latest article, I have shared a data readiness blueprint where you can learn how to build a data foundation that will take your AI initiatives to new heights. Link in the comments 🔗 Let 5X assess your data maturity and create a personalized plan to help build your AI strategy. Drop a comment below, or let's connect and chat!

Here’s my shoutout to the Top 10 data leaders who’re doing so much for the data & analytics. They are tons of others but I wanted to highlight some of my favorite: ↳ Seattle Data Guy - Benjamin Rogojan: Practical, actionable advice about data consulting or getting started with data He’s your guy! ↳ Zach Wilson: Runs one of the most successful data bootcamps @DataExpert.io ↳ David Jayatillake: His insights on where we are heading and the work behind the Semantic layer is inspiring me to think bigger ↳ Benn Stancil: The king of data-led storytelling, and I’m taking notes. ↳ Ahmed Elsamadisi’s Activity Schema is the global gold standard for innovative data modeling. ↳ Sadie St. Lawrence: Through Women in Data, she has been doing some amazing work in empowering women to thrive in data and AI. ↳ Julien Le Dem: As an open source leader Julien has been behind some of the biggest open source projects - Parquet, Open Lineage and Arrow ↳ Ananth P.’s “Data Engineering Weekly” is a must-read resource for those in the data industry. ↳ Joe Reis 🤓: Fundamentals of Data Engineering is the holy grail for anyone diving into the world of data engineering. And lastly, here’s a special mention for my co-founder Qi Wu, whose vision and drive have made 5X everything it is today. Who do you think I am missing on the list?

We're going to start to see a lot more 2-3 person data departments. I used to think big data teams were the key to success. Here's why I was wrong.. A decade ago, data was the Wild West! And, "Data Engineer" was the new kid on the block. We had Facebook, Yelp, mountains of data... and zero tools to make any relevant sense of it. That's when the specialists swooped in: analysts, engineers, scientists, each with their own niche. Back in 2020, it felt revolutionary. But that complexity got expensive. Big teams, big budgets, not always big results. Then, 18 months ago, the tides turned. Efficiency over excess: Now, We've entered the era of "Less is more" 'Fully Managed' platforms are replacing a dozen vendors. Instead of building a data platform team, you can focus on the application of data at the very start. And with that, we're seeing the rise of the #datageneralist. These folks aren't boxed into one role. They're the "Jack of All Trades" of data, adapting to whatever the business needs. Data generalists will replace... 1) Platform engineers: Managed Data Platforms like 5X means no need to have a data infra team because you get everything out of the box 2) Data engineers: Building pipelines is a thing of the past. Data engineers today can spend all their times on building data models and even BI. 3)Analysts/Engineers: Another name for Data engineer, we no longer need a specialized role for this. 4) Scientists: AI platforms are doing the heavy lifting. Now, it's about applying insights, not building them 5) BI folks: Forget building dashboards, Conversational BI and LLM chat are here to answer your questions. What else are we ready to let go of? Here's me in 2012 before I getting the less is more memo 😂

Do yourself a favor and learn to say NO! When you join a new data team, it's natural to want to prove your worth by saying yes to every request. Be wise with your time and focus on impactful projects. Overcommitting can lead to burnout and missed deadlines. One of the hardest skills to master is saying no, but it's essential for long-term success. Couldn't agree more with Christophe Blefari on this! Link to the full interview in the comment section